Comparison of treatments with ordinal responses in trials with sequential monitoring and response-adaptive randomization.

In clinical trials, comparisons of treatments with ordinal responses are frequently conducted using the proportional odds model. However, the use of this model necessitates the adoption of the proportional odds assumption, which may not be appropriate. In particular, when responses are skewed, the use of the proportional odds model may result in a markedly inflated type I error rate. The latent Weibull distribution has recently been proposed to remedy this problem, and it has been demonstrated to be superior to the proportional odds model, especially when response-adaptive randomization is incorporated. However, there are several drawbacks associated with the latent Weibull model and the previously suggested response-adaptive treatment randomization scheme. In this paper, we propose the modified latent Weibull model to address these issues. Based on the modified latent Weibull model, the original response-adaptive design was also revised. In addition, the group sequential monitoring mechanism was included to enable interim analyses to be performed to determine, during a trial, whether a specific treatment is significantly more effective than another. If so, this will enable the trial to be terminated at a much earlier stage than a trial based on a fixed sample size. We performed a simulation study that clearly demonstrated the merits of our proposed framework. Furthermore, we redesigned a clinical study to further illustrate the advantages of our response-adaptive approach.

Atypical Renal Clearance of Nanoparticles Larger Than the Kidney Filtration Threshold.

In recent years, several publications reported that nanoparticles larger than the kidney filtration threshold were found intact in the urine after being injected into laboratory mice. This theoretically should not be possible, as it is widely known that the kidneys prevent molecules larger than 6-8 nm from escaping into the urine. This is interesting because it implies that some nanoparticles can overcome the size limit for renal clearance. What kinds of nanoparticles can "bypass" the glomerular filtration barrier and cross into the urine? What physical and chemical characteristics are essential for nanoparticles to have this ability? And what are the biomolecular and cellular mechanisms that are involved? This review attempts to answer those questions and summarize known reports of renal-clearable large nanoparticles.

Adaptive treatment allocation for comparative clinical studies with recurrent events data.

In long-term clinical studies, recurrent event data are sometimes collected and used to contrast the efficacies of two different treatments. The event reoccurrence rates can be compared using the popular negative binomial model, which incorporates information related to patient heterogeneity into a data analysis. For treatment allocation, a balanced approach in which equal sample sizes are obtained for both treatments is predominately adopted. However, if one treatment is superior, then it may be desirable to allocate fewer subjects to the less-effective treatment. To accommodate this objective, a sequential response-adaptive treatment allocation procedure is derived based on the doubly adaptive biased coin design. Our proposed treatment allocation schemes have been shown to be capable of reducing the number of subjects receiving the inferior treatment while simultaneously retaining a test power level that is comparable to that of a balanced design. The redesign of a clinical study illustrates the advantages of using our procedure.

Response-adaptive treatment allocation for survival trials with clustered right-censored data.

A comparison of 2 treatments with survival outcomes in a clinical study may require treatment randomization on clusters of multiple units with correlated responses. For example, for patients with otitis media in both ears, a specific treatment is normally given to a single patient, and hence, the 2 ears constitute a cluster. Statistical procedures are available for comparison of treatment efficacies. The conventional approach for treatment allocation is the adoption of a balanced design, in which half of the patients are assigned to each treatment arm. However, considering the increasing acceptability of responsive-adaptive designs in recent years because of their desirable features, we have developed a response-adaptive treatment allocation scheme for survival trials with clustered data. The proposed treatment allocation scheme is superior to the balanced design in that it allows more patients to receive the better treatment. At the same time, the test power for comparing treatment efficacies using our treatment allocation scheme remains highly competitive. The advantage of the proposed randomization procedure is supported by a simulation study and the redesign of a clinical study.

Comparison of two treatments with skewed ordinal responses.

In clinical studies, the proportional odds model is widely used to compare treatment efficacies when the responses are categorically ordered. However, this model has been shown to be inappropriate when the proportional odds assumption is invalid, mainly because it is unable to control the type I error rate in such circumstances. To remedy this problem, the latent normal model was recently promoted and has been demonstrated to be superior to the proportional odds model. However, the application of the latent normal model is limited to compare treatments with similar underlying distributions except possibly their means and variances. When the underlying distributions are very different in skewness, both of the aforementioned procedures suffer from the undesirable inflation of the type I error rate. To solve the problem for clinical studies with ordinal responses, we provide a viable solution that relies on the use of the latent Weibull distribution, which is a member of the log-location-scale family. The proposed model is able to control the type I error rate regardless of the degree of skewness of the treatment responses. In addition, the power of the test also outperforms that of the latent normal model. The testing procedure draws on newly developed theoretical results related to latent distributions from the location-scale family. The testing procedure is illustrated with two clinical examples.

Noninferiority Studies with Multiple New Treatments and Heterogeneous Variances.

The objective of a noninferiority (NI) trial is to affirm the efficacy of a new treatment compared with an active control by verifying that the new treatment maintains a considerable portion of the treatment effect of the control. Compensation by benefits other than efficacy is usually the justification for using a new treatment, as long as the loss of efficacy is within an acceptable margin (NI margin) from the standard treatment. A popular approach is to express this margin in terms of the efficacy difference between the new treatment and the active control. Based on this approach and the realization that NI trials often comprise several new treatments, statistical procedures that simultaneously conduct NI tests of several new treatments have been developed. However, these procedures rely on the assumption that the variances of the treatments are homogeneous. In this article, we discuss the undesirable effect of using these procedures on the familywise Type I error rate when the treatment responses have heterogeneous variances. To alleviate this problem, we reveal potential procedures that are more appropriate. Further, a power study is conducted to compare the different procedures to provide guidance on the selection of adequate testing procedures in NI trials. Clinical examples are given for illustrative purposes.

Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses.

In clinical studies, multiple comparisons of several treatments to a control with ordered categorical responses are often encountered. A popular statistical approach to analyzing the data is to use the logistic regression model with the proportional odds assumption. As discussed in several recent research papers, if the proportional odds assumption fails to hold, the undesirable consequence of an inflated familywise type I error rate may affect the validity of the clinical findings. To remedy the problem, a more flexible approach that uses the latent normal model with single-step and stepwise testing procedures has been recently proposed. In this paper, we introduce a step-up procedure that uses the correlation structure of test statistics under the latent normal model. A simulation study demonstrates the superiority of the proposed procedure to all existing testing procedures. Based on the proposed step-up procedure, we derive an algorithm that enables the determination of the total sample size and the sample size allocation scheme with a pre-determined level of test power before the onset of a clinical trial. A clinical example is presented to illustrate our proposed method.

Pairwise comparisons with ordered categorical data.

Clinical trials frequently involve pairwise comparisons of different treatments to evaluate their relative efficacy. In this study, we examine methods for conducting pairwise tests of treatments with ordered categorical responses. A modified version of the Wilcoxon-Mann-Whitney test based on a logistic regression model assuming proportional odds is a popular choice for comparing two treatments. This paper discusses the extension of this test to pairwise comparisons involving more than two treatments. However, when the proportional odds assumption is not valid, the Wilcoxon-Mann-Whitney-type test procedure cannot control the overall type I error rate at the prespecified level of significance. We therefore propose a better strategy in which a latent normal model is employed. We presented a simulated comparative study of power and the overall type I error rate to illustrate the superiority of the latent normal model. Examples are also given for illustrative purposes.

Extension of three-arm non-inferiority studies to trials with multiple new treatments.

Non-inferiority (NI) trials are becoming increasingly popular. The main purpose of NI trials is to assert the efficacy of a new treatment compared with an active control by demonstrating that the new treatment maintains a substantial fraction of the treatment effect of the control. Most of the statistical testing procedures in this area have been developed for three-arm NI trials in which a new treatment is compared with an active control in the presence of a placebo. However, NI trials frequently involve comparisons of several new treatments with a control, such as in studies involving different doses of a new drug or different combinations of several new drugs. In seeking an adequate testing procedure for such cases, we use a new approach that modifies existing testing procedures to cover circumstances in which several new treatments are present. We also give methods and algorithms to produce the optimal sample size configuration. In addition, we also discuss the advantages of using different margins for the assay sensitivity test between the active control and the placebo and the NI test between the new treatments and the active control. We illustrate the new approach by using data from a clinical trial.

Response-adaptive treatment randomization for multiple comparisons of treatments with recurrent event responses.

Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures.

Sample size determination in step-up testing procedures for multiple comparisons with a control.

Step-up procedures have been shown to be powerful testing methods in clinical trials for comparisons of several treatments with a control. In this paper, a determination of the optimal sample size for a step-up procedure that allows a pre-specified power level to be attained is discussed. Various definitions of power, such as all-pairs power, any-pair power, per-pair power and average power, in one- and two-sided tests are considered. An extensive numerical study confirms that square root allocation of sample size among treatments provides a better approximation of the optimal sample size relative to equal allocation. Based on square root allocation, tables are constructed, and users can conveniently obtain the approximate required sample size for the selected configurations of parameters and power. For clinical studies with difficulties in recruiting patients or when additional subjects lead to a significant increase in cost, a more precise computation of the required sample size is recommended. In such circumstances, our proposed procedure may be adopted to obtain the optimal sample size. It is also found that, contrary to conventional belief, the optimal allocation may considerably reduce the total sample size requirement in certain cases. The determination of the required sample sizes using both allocation rules are illustrated with two examples in clinical studies.

Response-adaptive treatment allocation for clinical studies with ordinal responses.

Ordinal responses are common in clinical studies. Although the proportional odds model is a popular option for analyzing ordered-categorical data, it cannot control the type I error rate when the proportional odds assumption fails to hold. The latent Weibull model was recently shown to be a superior candidate for modeling ordinal data, with remarkably better performance than the latent normal model when the data are highly skewed. In clinical trials with ordinal responses, a balanced design is common, with equal sample allocation for each treatment. However, a more ethical approach is to adopt a response-adaptive allocation scheme in which more patients receive the better treatment. In this paper, we propose the use of the doubly adaptive biased coin design to generate treatment allocations that benefit the trial participants. The proposed treatment allocation scheme not only allows more patients to receive the better treatment, it also maintains compatible test power for the comparison of treatment efficiencies. A clinical example is used to illustrate the proposed procedure.

Adaptive Designs for Non-inferiority Trials with Multiple Experimental Treatments.

The increase in the popularity of non-inferiority clinical trials represents the increasing need to search for substitutes for some reference (standard) treatments. A new treatment would be preferred to the standard treatment if the benefits of adopting it outweigh a possible clinically insignificant reduction in treatment efficacy (non-inferiority margin). Statistical procedures have recently been developed for treatment comparisons in non-inferiority clinical trials that have multiple experimental (new) treatments. An ethical concern for non-inferiority trials is that some patients undergo the less effective treatments; this problem is more serious when multiple experimental treatments are included in a balanced trial in which the sample sizes are the same for all experimental treatments. With the aim of giving fewer patients the inferior treatments, we propose a response-adaptive treatment allocation scheme that is based on the doubly adaptive biased coin design. The proposed adaptive design is also shown to be superior to the balanced design in terms of testing power.

Testing of non-inferiority and superiority for three-arm clinical studies with multiple experimental treatments.

The purpose of a non-inferiority trial is to assert the efficacy of an experimental treatment compared with a reference treatment by showing that the experimental treatment retains a substantial proportion of the efficacy of the reference treatment. Statistical methods have been developed to test multiple experimental treatments in three-arm non-inferiority trials. In this paper, we report the development of procedures that simultaneously test the non-inferiority and the superiority of experimental treatments after the assay sensitivity has been established. The advantage of the proposed test procedures is the additional ability to identify superior treatments while retaining an non-inferiority testing power comparable to that of existing testing procedures. Single-step and stepwise procedures are derived and then compared with each other to determine their relative testing power and testing error in a simulation study. Finally, the suggested procedures are illustrated with two clinical examples.

Step-up procedures for non-inferiority tests with multiple experimental treatments.

Non-inferiority (NI) trials are becoming more popular. The NI of a new treatment compared with a standard treatment is established when the new treatment maintains a substantial fraction of the treatment effect of the standard treatment. A valid NI trial is also required to show assay sensitivity, the demonstration of the standard treatment having the expected effect with a size comparable to those reported in previous placebo-controlled studies. A three-arm NI trial is a clinical study that includes a new treatment, a standard treatment and a placebo. Most of the statistical methods developed for three-arm NI trials are designed for the existence of only one new treatment. Recently, a single-step procedure was developed to deal with NI trials with multiple new treatments with the overall familywise error rate controlled at a specified level. In this article, we extend the single-step procedure to two new step-up procedures for NI trials with multiple new treatments. A comparative study of test power shows that both proposed step-up procedures provide a significant improvement of power when compared to the single-step procedure. One of the two proposed step-up procedures also allows the flexibility of allocating different error rates between the sensitivity hypothesis and the NI hypotheses so that the assignment of fewer patients to the placebo becomes possible when designing NI trials. We illustrate the new procedures using data from a clinical trial.

Multiple comparisons with a control for a latent variable model with ordered categorical responses.

Ordered categorical data are frequently encountered in clinical studies. A popular method for comparing the efficacy of treatments is to use logistic regression with the proportional odds assumption. The test statistic is based on the Wilcoxon-Mann-Whitney test. However, the proportional odds assumption may not be appropriate. In such cases, the probability of rejecting the null hypothesis is much inflated even though the treatments have the same mean efficacy. An alternative approach that does not rely on the proportional odds assumption is to conceptualize the responses as manifestations of some underlying continuous variables. However, statistical procedures were developed only for the comparison of two treatments. In this article, we derive testing procedures that compare several treatments to a control, utilizing a latent normal distribution with the latent variable model. The proposed procedure is useful because multiple comparisons with a control is very frequently an objective of a clinical study. Data from clinical trials are used to illustrate the proposed procedures.

A unified framework for the comparison of treatments with ordinal responses.

Different latent variable models have been used to analyze ordinal categorical data which can be conceptualized as manifestations of an unobserved continuous variable. In this paper, we propose a unified framework based on a general latent variable model for the comparison of treatments with ordinal responses. The latent variable model is built upon the location-scale family and is rich enough to include many important existing models for analyzing ordinal categorical variables, including the proportional odds model, the ordered probit-type model, and the proportional hazards model. A flexible estimation procedure is proposed for the identification and estimation of the general latent variable model, which allows for the location and scale parameters to be freely estimated. The framework advances the existing methods by enabling many other popular models for analyzing continuous variables to be used to analyze ordinal categorical data, thus allowing for important statistical inferences such as location and/or dispersion comparisons among treatments to be conveniently drawn. Analysis on real data sets is used to illustrate the proposed methods.

Three p-value consistent procedures for multiple comparisons with a control in direction-mixed families.

In clinical studies, it is common to compare several treatments with a control. In such cases, the most popular statistical technique is the Dunnett procedure. However, the Dunnett procedure is designed to deal with particular families of inferences in which all hypotheses are either one sided or two sided. Recently, based on the minimization of average simultaneous confidence interval width, a single-step procedure was derived to handle more general inferential families that contained a mixture of one- and two-sided inferences. But that single-step procedure is unable to guarantee the condition of p-value consistency which means that when a hypothesis with a certain p-value is rejected, all other hypotheses with smaller p-values are also rejected. In this paper, we present a single-step procedure and two stepwise procedures which are p-value consistent. The two proposed stepwise procedures provide more powerful testing methods when compared with single-step procedures. The extent of their superiority is demonstrated with a simulation study of average power. Selected critical values are tabulated for the implementation of the three proposed procedures. Additional simulation studies provide evidence that the new stepwise procedures are robust to moderate changes in the underlying probability distributions, and the proposed step-up procedure is uniformly more powerful than the resampling-based Hochberg step-up approach in all considered distribution models. Finally, we provide a practical example with sample data extracted from a medical experiment.

Multiple testing to establish superiority/equivalence of a new treatment compared with k standard treatments for unbalanced designs.

In clinical studies, multiple superiority/equivalence testing procedures can be applied to classify a new treatment as superior, equivalent (same therapeutic effect), or inferior to each set of standard treatments. Previous stepwise approaches (Dunnett and Tamhane, 1997, Statistics in Medicine16, 2489-2506; Kwong, 2001, Journal of Statistical Planning and Inference 97, 359-366) are only appropriate for balanced designs. Unfortunately, the construction of similar tests for unbalanced designs is far more complex, with two major difficulties: (i) the ordering of test statistics for superiority may not be the same as the ordering of test statistics for equivalence; and (ii) the correlation structure of the test statistics is not equi-correlated but product-correlated. In this article, we seek to develop a two-stage testing procedure for unbalanced designs, which are very popular in clinical experiments. This procedure is a combination of step-up and single-step testing procedures, while the familywise error rate is proved to be controlled at a designated level. Furthermore, a simulation study is conducted to compare the average powers of the proposed procedure to those of the single-step procedure. In addition, a clinical example is provided to illustrate the application of the new procedure.

Multiple comparisons with a control in families with both one-sided and two-sided hypotheses.

Comparing several treatments with a control is a common objective of clinical studies. However, existing procedures mainly deal with particular families of inferences in which all hypotheses are either one- or two-sided. In this article, we seek to develop a procedure which copes with a more general testing environment in which the family of inferences is composed of a mixture of one- and two-sided hypotheses. The proposed procedure provides a more flexible and powerful tool than the existing method. The superiority of this method is also substantiated by a simulation study of average power. Selected critical values are tabulated for the implementation of the proposed procedure. Finally, we provide an illustrative example with sample data extracted from a medical experiment.